Resource allocation and risk modeling for geographically distributed assets

ABSTRACT

A risk exposure model is developed for network or moveable assets not specific to a single, fixed address or location. An asset map using a plurality of geographic representation points is used to identify the physical locations of the asset portions (or possible physical locations in the case of a moveable asset). Baseline geographic, geologic, political, and demographic data is similarly represented using geographic representation points. Meta-data associated with each geographic representation point is used to identify details related to the asset or baseline feature at that geographic location. Risk exposure values are then calculated using the geographic representation points specific to the asset portions that are subject to risks associated with the location of the asset portion.

BENEFIT CLAIM

This application claims the benefit as a continuation of applicationSer. No. 14/070,843, filed Nov. 4, 2013, which claims the benefit ofprovisional application 61/779,206, filed Mar. 13, 2013, the entirecontents of which are hereby incorporated by reference for all purposesas if fully set forth herein.

FIELD OF THE DISCLOSURE

Embodiments of the present disclosure relate generally to systems andmethods for apportioning resources among geographically distributedparts of a large facility or asset, such as a railroad system. Morespecifically, embodiments of the present disclosure relate to resourceallocation and risk modeling for geographically distributed assets usinggeographic representation points for the asset.

BACKGROUND

Many municipalities, governmental units, and private businesses haveassets located at a variety of locations, such as factories located inseveral cities across the country or around the world. For variousreasons, it may be important to consider risks to these locations andallocation of resources among such facilities. Such geographicallydistributed facilities may be thought of based on the area that theycover (e.g., the Midwest region restaurants of a fast food chain) orbased on the network that they define (e.g., the network of an electricpower distribution company).

As a specific example, a company that supplies ground transportationprovides tractor-trailers to multiple ports around a country for haulingimports and products from each port to an inland destination. There arenumerous reasons to supply the ports with a certain number oftransportation assets (e.g., tractor-trailers, cargo trailers, loadingequipment, repair equipment) which changes over time. The assetssupplied to each port may differ for any of a variety of reasons thatmay or may not remain constant. For example, if one set of ports areexperiencing labor difficulties, there may be need to dynamically shifttransportation assets to another port as cargo ships are diverted, andthen shift them back as labor issues are resolved.

In another example, a utility company may attempt to anticipate thepotential impact of inclement weather and gauge the appropriateresponse, limited by finite funds and/or resources. Because of thislimitation, the utility company strives to identify areas of its networkat the greatest risk to a variety of issues, such as trees felled bywind or snow. In this setting, the utility company attempts to determinethe optimal mix of spare equipment (e.g., poles, wire, transformers)used to respond to the event, and appropriately distribute the spareequipment across a number of staging areas (perhaps 50 locationsthroughout a geographic service area). Similarly, the utility companystrives to optimize limited funds for engaging third parties (e.g., treeservice contractors) to perform preventative maintenance along thousandsof miles of roadways within its service area.

As another example, a fast food restaurant chain may have severalhundred locations around the country. The headquarters of the companymust determine, based on a wide variety of factors, how much of eachfood item to supply to each restaurant.

Such determinations apply in a wide variety of situations. For instance,aid organizations (e.g., the Red Cross, FEMA) maintain stocks of variousdisaster relief items in various warehouses. When a major weather eventsuch as a hurricane is forecast, it may be advantageous to move suppliesfrom one warehouse (e.g., in an area not likely to be impacted) toanother (e.g., closer to the area likely to be impacted).Counter-intuitively, in some situations it may also be important to movesupplies away from an area likely to be impacted, particularly if thereis a threat that the supplies will be compromised by the catastrophicevent if left at their current location.

Consider the operations of a railroad or municipal transportationauthority. Knowing where to store operating equipment and stage spareequipment (rail, railcars, electrical transformers, and the like) can becritical to reducing downtime in the event of a catastrophic event, suchas the storm surge that impacted the New York Subway system as a resultof Tropical Storm Sandy in 2012.

Similar modeling and planning can be used in other industries as well.The insurance industry may well seek to model the impact of catastrophicevents on various insured properties. In that industry, multiple layersof insurers have often-overlapping coverage, all with limits (e.g.,caps) and other constraints. Further, catastrophic events, even ifrandomly distributed, are sometimes bunched so that exposure seemsunusually high. In addition, some catastrophic events tend not to beindependent but instead are tied together, e.g., (a) a weather patternbreeds multiple cyclonic events during a single season; (b) a largeearthquake is accompanied by a tsunami and numerous aftershocks; (c) aterrorist attack is not isolated but is planned as one of severalcoordinated attacks. Continuous geographic distribution of insuredassets such as a rail system complicates planning in various ways, sointerest in modeling is particularly great in the insurance industry.

Determining the geospatial locations and how to best to allocateresources (e.g., electrical wires or train rails) to geographicallydiverse assets has traditionally been accomplished as a combination ofgeocoding and operations research. Geocoding conventionally useslocation information such as an address or latitude/longitudecoordinates as a representation of each asset under consideration (e.g.,each fast food restaurant). Operations research takes a number offactors, including the location information, as a means to optimize theallocation of assets.

However, not all assets are readily described or optimized in thismanner. Railroads, utility transmission lines, gas and oil pipelines andthe like are continuously distributed throughout their geographic range,and in any event often do not have conventional physical addressescorresponding to the locations of their component parts. Many variables,such as the value of infrastructure, are not intended to be “optimized,”but rather just allocated.

The New York City Subway system, for example, has some two dozen railyards, in addition to more than 200 miles of track on its two dozen orso routes. Some of these rail yards have dozens of tracks, with all ofthe associated switching devices and controls. Thus, the amount of spareequipment needed nearby to restore operation to the yards after acatastrophic event may be orders of magnitude more for the yards thanfor the route segments of the system. However, unlike the food deliveryrequirements for a group of fast food restaurants, the distribution ofresource needs for the New York Subway system are based on continuous(rather than discrete) geographical distributions.

Consider now an insurance perspective on an asset that has continuousgeographic distribution, such as the New York Subway system. Usingcomputerized models, underwriters seek to price risk based on theevaluation of the probability of loss for a particular location andproperty type as well as manage portfolios of risks according to thedegree to which losses correlate from one location to another as part ofthe same catastrophe event. These probabilistic (stochastic) catastrophemodels include, but are not limited to, earthquake, fire followingearthquake, tropical cyclone (hurricanes, typhoons, and cyclones),extra-tropical cyclone (windstorm), storm-surge, river flooding,tornadoes, hailstorms, terrorism and other types of catastrophe events.These catastrophe models are built upon detailed geographical databasesdescribing highly localized variations in hazard characteristics, aswell as databases capturing property and casualty inventory, buildingstock, and insurance exposure information.

Modeling systems using these models allow catastrophe managers,analysts, underwriters and others in the insurance markets (andelsewhere) to capture risk exposure data, to analyze risk for individualaccounts or portfolios, to monitor risk aggregates, and to set businessstrategy. Typical catastrophe modeling systems are built around ageographical model comprising exposure information for individuallocations, specific bounded locations or areas. These locations or areasof interest are typically defined using for example, a street address,postal code boundaries, including ZIP codes, city (or otheradministrative) boundaries, electoral or census ward boundaries andsimilar bounded geopolitical subdivisions.

A drawback of using these types of mechanisms (e.g., postal boundaries,cities, municipalities, building IDs, or ZIP codes) to define locationsor areas is that some portions of an asset may not have an address orrepresentative geopolitical boundaries that can be used to meaningfullycharacterize their corresponding risk exposures. Indeed, some portionsof the asset (e.g, train cars, locomotives, cargo) may themselves bemoveable properties without a fixed location.

Another drawback of these types of mechanisms to define locations orareas is that they do not allow the system or user the flexibility toselect different resolutions that would provide the better geospatialrepresentations of the asset. In addition, it may be very difficult toidentify a single location that characterizes the risk of the wholegeographic area.

These and other drawbacks exist. For asset portions having a fixedlocation but not a corresponding conventional address, use of a proxysuch as ZIP code may result in extremely poor asset allocations andmodeling results. For asset portions that are moveable, modeling thatassumes the asset to be at a single geographic location again may poorlyrepresent the actual exposure for any particular catastrophic event.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic representation of a network asset that is aportion of a rail system which includes asset portions or sub-assetsaccording to one embodiment.

FIG. 1B is an illustration of a network asset having multiple sub-assetsthat is analyzed using a variable resolution grid according to oneembodiment.

FIG. 2A is an illustration of a network asset map that has beenoverlayed with a baseline map, thereby creating a combined map showingassets in the context of baseline features, according to one embodiment.

FIG. 2B is an illustration of using geographic representation points tocharacterize intersections of a network asset with baseline featuresaccording to one embodiment.

FIG. 3 is a flow diagram of a method for calculating risk exposurevalues using a network asset map, a baseline map, and their associatedmeta-data, according to one embodiment.

FIGS. 4A and 4B illustrate a network environment and a systemarchitecture, respectively, of a system for calculating risk exposurevalues according to one embodiment.

FIG. 5 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller) according to one embodiment.

DETAILED DESCRIPTION OF THE DRAWINGS

Embodiments described herein include methods and systems for developingrisk exposure models for assets that are networks or are moveable andtherefore are not specific to an address or a single location. Theembodiments described herein can also be applied to resource allocationmodeling for supplying or servicing asset portions of a distributedasset. The models and systems herein use an asset map to describe thedistributed nature of a network or a moveable asset. An asset mapcharacterizes the asset using a plurality of geographic representations(points, lines, bounded areas) to identify the physical locations ofportions of the network asset (or possible physical locations in thecase of a moveable asset). Meta-data is associated with each geographicrepresentation point reciting details related to the asset portion atthat geographic location. Examples of meta-data include geographiccoordinates (e.g., GPS coordinates, latitude/longitude) of the assetportion, asset capacity, asset flow or directionality, assetconnectivity within and to other asset portions, asset portion type(e.g., network, electrical grid substation, bridge, tunnel, storage,maintenance), and others.

The models and systems also include a baseline map that describes thecontext in which the network or moveable asset portions are disposed.For example, the baseline map can include information typicallydescribed in a GIS map such as public infrastructure (e.g., roads,bridges), private infrastructure (e.g., electrical grid elements), andfeatures of the natural landscape (e.g., waterways, flood zones,earthquake faults), as well as political boundaries, population density,demographics, and other similar information. As with the assets, thesefeatures of the baseline map can also be described by meta-data thatcharacterizes the features.

The asset map and the baseline map are used in cooperation to identifyintersections between asset portions and risks posed by baselinefeatures (such as geographical features or the physical surroundings ofan asset portion), thereby using the intersection and the meta-dataassociated with the intersecting asset and baseline feature to quantifythe risk (also termed a “risk exposure”) to the asset as well as a riskexposure value (i.e., the potential financial liability that may beincurred by a party or parties by destruction or damage to the assetfrom a catastrophe). These intersections are described as “points” inthis disclosure.

The terms “geographic representations” and/or “geospatialrepresentations” are used in this disclosure to generically describepolygons, single or multi-segment lines, points or other geometricstructures that capture the physical outline of an asset and/or abaseline feature. The term “map” is used solely for convenience ofexplanation. It will be understood that the generation of a map fromdata is not necessary, and that the methods and systems described belowcan determine risk exposure values using data in other forms, not merelygraphically or visually represented data.

The methods and systems described herein can also be used for supplychain management by forecasting demand and consumption of discreteassets distributed over a large area (e.g., a set of restaurants of achain distributed throughout a geographic region). Categories of useinclude, but are not limited to, personnel assignments, resource andasset (e.g. spare equipment) allocation, maintenance scheduling andcompletion, location planning and business case support (particularlyfor larger commercial and industrial facilities where criticalinfrastructure failures can interrupt operations), corporate disasterplanning and response, and analysis of other similar scenarios involvingallocation of limited or time-sensitive resources.

Geographic Representation Points of Network and Moveable Assets

FIG. 1A is a schematic representation a portion of a rail system 100,which in this example is described as a network asset because itsphysical infrastructure is localized (e.g., to rail beds and/orbuildings) but is also distributed over a geographic area. The railsystem includes at least one rail 104, a switching yard 108, amaintenance facility 112, and a customer loading site 116 (describedcollectively as “sub-assets” of the rail system 100).

The various example components of the rail system 100 that are shownillustrate the diversity of sub-asset types that are included in therail system 100 asset as a whole, and also can illustrate the variationin both time and geographic location of the value (and therefore therisk exposure value) of the sub-assets. For example, a value of theswitching yard 108, and similarly a value of the maintenance facility112 will be much higher when multiple trains are at one of theselocations at the same time. Correspondingly, the risk exposure valuewill be higher in this case because, in the event of a catastrophe, thefinancial loss of multiple trains as well as the physical structure ofthe switching yard 108 will be higher than the financial loss of thephysical structure of the switching yard alone. Similarly, a value ofthe customer loading site 116 is higher when a train is at the customerloading site and the site itself contains inventory. This is in contrastto a value of the customer loading site 116 after a train loaded withinventory has departed, leaving the site as merely an empty warehouse.As in the example of the switching yard 108, the risk exposure value ishigher when the train is at the customer loading site 116 and the siteis filled with inventory.

Alternatively, the asset of a train and an asset of the switching yard108 can be treated as separate assets using the methods and systemsdescribed herein. That is, because the methods and systems herein can beapplied to moveable assets, a risk exposure value of a train can bedetermined and/or calculated as a function of its location, and alsomaintained separately from the switching yard 108 asset.

Two features of the present disclosure are used to describe thesevariations in value and risk as a function of both time and geographiclocation: a variable resolution grid and geographic representationpoints.

A variable resolution grid can be used to provide levels of detail to anasset map and/or a baseline map proportional to the value of an asset, adensity of assets at a particular map location, and/or a level of riskthat is a function of conditions local to all spans or an area of theasset. In other words, a variable resolution grid provides a way offocusing specific concentrations of exposures on a geographical grid todetermine projected loss caused by a catastrophe. Other embodiments ofvariable resolution include a user-selectable “uniform resolution grid,”a user-selectable line interval (rather than a grid), user-selectableregular intervals for use with the geographic representation points of adistributed asset, and other similar configurations. FIG. 1B illustratesan application of a variable resolution grid to the rail system 100 ofFIG. 1A. In this case, the finest resolution of the grid 120 iscoincident with the assets having a highest density of asset valueand/or asset risk, in this case the switching yard 108. Variableresolution grids and their applications are described in U.S. Pat. No.8,229,766, which is incorporated by reference in its entirety.

Geographic representation points, illustrated as the filled circlesshown in FIG. 2B, are used to represent the intersections between thegeographic representation of an asset (in this case a rail system) and abaseline feature(s). The benefits of using these geographicrepresentation points are two-fold. First, the network nature of theasset can be matched to baseline data (e.g., geographic features, publicinfrastructure, and geologic features) thereby identifying specificnetwork asset geographic locations to geographically specific riskfactors. This will be described in more detail in the context of FIGS.2A, 2B, and 3. Second, each of the geographic representation points canhave associated meta-data that quantifies the asset (or sub-asset) type,location, and other characteristics that can be used to quantifycharacteristics of the asset or be used with other information toquantify the risk exposure value associated with a geographicalrepresentation point. Third, the number of geographic representationpoints and the interval between them is proportional to the density ofintersections between the asset portions and the risks posed by baselinefeatures.

Baseline Maps and Risk Value Exposure Determination Method

FIG. 2A is a schematic illustration of a combined map 200 that includesthe portions of the rail system 100 of FIG. 1A that has beensuperimposed on a baseline map showing, in this example, geologicfeatures that can pose risks to the asset and/or sub-assets. Thecombined map 200 of this example shows not only the sub-assets of FIG.1A, but also a stream 204 and a 20-year flood zone 208 surrounding thestream.

As is shown, the stream 204 flows by the switching yard 108, indicatingthe locations of bridges and significant infrastructure relative toother portions of the assets. The addition of the 20-year flood zone 208to the combined map 200, which can be accessed using a publiclyavailable GIS database, indicates different levels of flooding risk tothe sub-assets. That is, because the switching yard 108 and themaintenance facility 112 are in the 20-year flood zone 208 surroundingthe stream 204 will reflect a higher flooding risk (and therefore ahigher risk exposure value) compared to the customer loading site 116,which is outside the flood zone.

FIG. 2B illustrates a set of geographic representation points 212 thatindicate intersections between the geographic representations of assetportions and baseline features. As described above, each point providesa user with meta-data describing the asset portion and the intersectingbaseline feature, including risks, locations, and the like. As alsodescribed above, the spacing between the geographic representationpoints of the set 212 varies as a function of the risks posed, thedensity of assets, and/or the asset value.

FIG. 3 shows a method 300 for creating a combined map and calculatingrisk exposure values using geographic representation points, theirassociated meta-data, and the meta-data associated with features of abaseline map. As described above in FIG. 1A, a map of geographicrepresentations (points, polygons, lines, multi-segment lines, etc.) ofa network asset (or data corresponding to and characterizing thelocations, features, etc. of a network asset) are identified 304. Themap includes the geographic locations and/or coordinates of the varioussub-assets and components of the network asset, reflecting discretelocations of asset portions. Associated with the geographicrepresentations of the network map are meta-data that describe,characterize, or identify a portion of the network at that point.

A baseline map is identified 308 that describes the various geographic,geologic, political, and/or other features characterizing the setting ofthe asset that can pose risks to the asset, or portions thereof. Asdescribed above, the baseline map also uses geographic representations(points, polygons, lines, multi-segment lines, etc.), and associatedmeta-data to describe the physical outline of the baseline feature andthe risk factors posed to the asset by the baseline feature. Returningto the example shown in FIG. 2, a multi-segment line can be used in thebaseline map to trace the path of the stream 204 and one or morepolygons can be used in the baseline map to identify the limits of the20-year flood zone 208. Meta-data associated with the lines representingstream 204 can include, for example, geographic end-points ofline-segments of the stream, its average flow rate, its flood stage flowrate, its flooding frequency, and other similar risk factors. Similarly,meta-data associated with the polygon representing the 20-year floodzone can include flooding frequency, flooding probability as a functionof location within the flood zone, typical flooding dates, distance fromlocal emergency services, and other similar information.

The network map and baseline maps (or non-graphical data) are overlayed312 (or otherwise associated with one another for combined analysis andother use) to form a combined map. The combined map, showing both assetand baseline features, can then be used to generate geographicrepresentation points 316 that are intersections of the geographicrepresentations of the asset (or portions thereof) and proximatebaseline features. It is these intersections of asset portions and riskfor which risk exposure values are calculated.

In some examples, multiple variable resolution grids are used witheither or both of the network map or the baseline map. The variableresolution grid, as described in U.S. Pat. No. 8,229,766 andincorporated by reference herein, provides a method for providing addeddetail to assets or baseline features when warranted. For example, someassets may have discrete concentrations of value (e.g., the switchingyard 108) and some baseline features may have discrete concentrations ofrisk (e.g., the 20-year flood plain 208). Using a variable resolutiongrid to support additional meta-data for these discrete locations ishelpful for creating an accurate risk exposure value. Furthermore, usinga variable resolution grid that lacks such meta-data for assets orbaseline features that do not warrant additional detail facilitates anefficient use of computational resources.

The meta-data associated with a geographic representation pointgenerally describes a location of a point in one or more coordinatesystems, such as a geographic coordinate system (e.g., by using a GPScoordinates, latitude and longitude, elevation) and/or a politicalcoordinate system (e.g., street address). The meta-data of a geographicrepresentation point also includes information describing the asset orsub-asset represented by the point. This information includes, but isnot limited, to an owner and/or operator of the asset and/or sub-asset,a value (which can also include a value as a function of time, asdescribed in the example of the switching yard 108), a description ortype of asset, a composition of the asset (e.g., rails, buildings,maintenance equipment), and other similar data used to quantify an assetvalue. In other examples the meta-data does not include asset value,which is supplied separately.

Upon generating 316 the geographic representation points of theintersections, the meta-data associated with both the asset (or portionsthereof) and the baseline feature are retrieved. These data can beretrieved from a private source (such as an insurance industry databaseor a common carrier database) or a public source (such as a governmentsponsored GIS database). Regardless of the source, these data are usedto quantify a value of the asset and establish risk factors due to theasset map and baseline features. These are then used to calculate 324 arisk exposure value.

The calculation 324 of a risk exposure value is performed usingconventional methods. For example, in one embodiment a user assigns aweighing factor representing duration, frequency, time of year orseasonality, asset type, baseline feature type, risk type or othersimilar risk or value factor. These weighing factors are used tocompensate for an absence of meta-data describing either or both of theasset or feature. These various weights are then multiplied to calculatea final weighing factor associated with the geographic representationpoint. The final weighing factor is then multiplied by the asset (orasset portion) value to determine the risk exposure value associated thegeographic representation point of the intersection.

In this way, the risk exposure value is assigned only to the portion ofthe network or distributed asset that is actually exposed to the riskand not inaccurately distributed to the entire asset (or to alarger-than-needed portion of the asset). The benefit of this method isthat risk exposure values are more precisely associated with specificsub-assets or portions of network assets or assets that are nototherwise assignable to a single, fixed address. Aspects of the method300 have already been provided above in the context of a network asset(rail system 100). Another particular application is for assets that aremoveable, such as inventory that is transported in a train car or atruck. The risk exposure to the inventory can change as a function ofthe location of the transporting vehicle, the time of year, route,duration of the trip, etc. For example, the risk exposure to inventorybeing transported by vessel during hurricane season in a coastal areacould be greater than the same inventory being transported on inlandrivers or lakes during the same time of year.

Risk Value Determination System and System Environment

FIG. 4A illustrates an example of a system environment 400 used forperforming the method 300. The system environment 400 includes a riskexposure system 404, described in detail in FIG. 4B, a network 408, abaseline database 412 and an asset database 416.

The network 408 is configured to permit communication between the riskexposure system 404 and other information sources, such as the baselinedatabase 412 and the asset database 416. The network 408 may compriseany combination of local area and/or wide area networks, using bothwired and wireless communication systems. In one embodiment, the network408 uses standard communications technologies and/or protocols. Thus,the network 408 may include links using technologies such as Ethernet,802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G,CDMA, digital subscriber line (DSL), etc. Similarly, the networkingprotocols used on the network 408 may include multiprotocol labelswitching (MPLS), transmission control protocol/Internet protocol(TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP) and file transfer protocol(FTP). Data exchanged over the network 408 may be represented usingtechnologies and/or formats including hypertext markup language (HTML)or extensible markup language (XML). In addition, all or some of linkscan be encrypted using conventional encryption technologies such assecure sockets layer (SSL), transport layer security (TLS), and InternetProtocol security (IPsec).

The baseline database 412 includes geographic, geologic, political, anddemographic data collected by public and/or private sources to describeand characterize environments in which assets are disposed. The baselinedatabase 412 and the information therein can be accessed by the riskexposure system 404 for information regarding baseline featuresdiscussed above.

In one example, the baseline database 412 is a GIS database that canprovide data renderable into a graphic format (i.e., a map) and alsoprovide meta-data that can be further used by the risk exposure system404 to quantify risks posed by baseline features. For example, somemeta-data includes seasonal fluctuations in water levels (such as instream 204), traffic patterns (for example, used to quantify the riskexposure to perishable freight transported by truck), crime rates, anddata that can otherwise influence risk exposure. The number of meta-dataelements can include size or position of an asset or a feature of anasset in multiple dimensions (height, width, depth, diameter,circumference), construction/composition (concrete, metal, plastic,optical fibers), operating capacities by season, minimum or maximumoperational limits associated with the asset (e.g., volume, pressure,frequency, flow rate), fragility, alternative routes, age, useful life,accuracy of location, directionality of flow (e.g., one way orbidirectional), ownership status (partially or wholly owned), commodityor cargo type, identifying names, geopolitical references, and otherinformation.

The asset database 416 stores data used by the risk exposure system 404to quantify the risk to an asset, or portion thereof. The data stored bythe asset database 416 includes the meta-data described above thatincludes, but is not limited to, GPS coordinates, latitude/longitude ofthe asset, asset type (e.g., moveable, network, electrical gridsubstation, train car, inventory shipment), and other characteristics.

FIG. 4B illustrates a system architecture of the risk exposure system404 used for calculating a risk exposure value of an asset portion, asdescribed above. The system architecture of the risk exposure system 404includes an asset database 420, a local baseline database 424, a queryengine 428, a combined map generator 432, a risk factor database 436,and a risk exposure value calculator 440.

The asset database 420 stores meta-data associated with network assets,moveable assets, or other assets that are distributed and not otherwiseassociated with a single, fixed, street address. As described above, themeta-data describing the asset and stored in the asset database 420includes the geospatial representations of the various asset portions orsub-assets, the relationship or connection between the various portionsof the assets to each other and the asset as a whole, types, and othersimilar characteristics or data used for the calculation of a riskexposure value. As with the other databases described below, the assetdatabase 420 can be a relational database or other type of data storagesystem used to store and retrieve data.

Similar to the asset database 420, the local baseline database 424stores data characterizing baseline features and the risks that thefeatures pose to the asset portions. Where the baseline database 412described above is an external database operated by a third party, suchas a government GIS database documenting the geographic limits of20-year flood zones, the local baseline database 424 permits the riskexposure system 404 to record and access information regarding baselinerisks identified by or recorded in the system 404 separately from thebaseline database 412. For example, referring again to FIG. 2, if therail switching yard 108 is known to flood more frequently than would beindicated by data related to the stream 204 and stored in baselinedatabase 412, this data can be stored at the local baseline database 424for use in the risk exposure value calculation.

Not only can private observations that enhance the understanding ofpublicly known risks be stored in the local baseline database 424, butalso risks known privately to the operator of the risk exposure system404 can also be recorded in the local baseline database. For example,risks specific to the asset itself (e.g., chemical spill, explosion,theft, arson) can be entered into the baseline database 424 and used inthe calculation of a risk exposure value. Similarly, the proximatelocation of risks that compounds the risk exposure value of other riskscan be identified. That is, the presence of hazardous waste, chemicals,or other volatile hazards has inherent risk, but also increases the riskexposure value of separate, but related, risks. For example, a traincarrying hazardous waste that derails causes more damage than a traincarrying plywood.

The query engine 428 is configured to communicate with data sourcesexternal to the risk exposure system 404, such as the baseline database412 and the asset database 416. In one example, the query engine 428 isan application programming interface (“API”) that provides functionalityfor exchanging data between the risk exposure system 404 and, forexample, the baseline database 412 and the asset database 416.

The combined map generator 432 receives data from the asset database 420regarding a particular asset, and also receives data from the localbaseline database 424 for a range of baseline features proximate to theasset geolocation. The combined map generator 432 may also receive datafrom the query engine 428 that is relevant to the asset and the baselinefeature but is stored externally to the system 404. The combined mapgenerator 432 identifies intersections of asset portions or sub-assetsand baseline features, thereby associating a risk factor from a specificbaseline feature relevant to a specific asset portion or sub-asset. Asdescribed above, the benefit of this is that risks specific to an assetportion are associated with the portion and not generic to the asset asa whole. The combined map generator can optionally produce a graphicdepiction of the asset portion and the baseline feature, as well astheir intersection, on a geographic, geologic, demographic, or politicalmap. Furthermore, the combined map generator 432 can generate geographicrepresentation points without using a variable resolution grid orbaseline map, instead creating a geographic representation point of theasset at regular intervals between a starting point and an endpoint.

The risk factor database 436 is used in connection with the combined mapgenerator 432 to quantify the risk factor to the asset that is posed bythe baseline feature by providing a weight to the associated risk. Theseweights are used to differentiate the geographic representation pointsusing the meta-data associated with each point. That is, the weights areused to determine, in part, the risk exposure value allocated to eachpoint.

Depending on the implementation environment, various weighting schemesare used in various embodiments. The weights can be applied to variousgeographic representation points using rules, asset values, orconditions provided by a user and/or automatically inferring the weightsfrom the meta-data. For example, a geographic representation pointassociated with a train rail crossing a river can have a higher weightthan a rail crossing an infrequently used or geographically remote road.In another example, risks of an interruption to the operations of abusiness caused by a delivery delay can be weighted based on thelocation of the delay relative to the delivery point, the downstreambusiness impacts that compound upon a delay, and other factors. Also,because of the variability in quality and quantity of meta-data, weightscan be used as proxies for missing meta-data, or as an override forexisting meta-data as applied to asset values instead of a geographicrepresentation point. As described above, multiple weights associatedwith a geographic representation point can be multiplied to provide asingle weight for a point.

The risk exposure value calculator 440 then calculates a risk exposurevalue using the meta-data stored in the asset database 420, the variousweighing factors, the local baseline database 424, and the risk factordatabase 436. The calculation involves two sets of weights, one from thecombined map generator 432 and one from the risk factor database 436,and a value of an asset from the asset database 420. In oneimplementation, weights from 436 are applied against to the asset valuefirst, causing an interim asset value allocation across a group or atype of geographic representation points. Weights for individual pointswithin the group or type are then applied to the interim asset values,thus generating a risk exposure value for each point. In someembodiments, weights are provided relative to some normalized level,such as a dynamically determined normalization point allowing gooddynamic range of weights above and below the normalization point.Depending on application, weighting factors may be provided as input tothe system in multiple forms (currency, percents, time, etc.); thoseskilled in the art will recognize that conversion to common forms may berequired in such situations. In other environments, multiple forms maybe supported directly (e.g., normalization points may be specified inboth miles and kilometers to avoid the need to convert individualmeasurements that may be supplied in either format).

Supply Chain Management Example

The described systems and methods can also be applied in other contextsin which portions or elements of a network have varying inputs and/oroutputs. For example, discrete restaurant locations can have rawmaterial needs that vary as a function of season, geography, customerdemographics, location, or other factors, including dependencies onpublic/commercial infrastructure and access to and/or from suppliers andcustomers. The systems and methods described above can be used to recordmeta-data describing the particular needs and/or patterns of thediscrete locations and provide the locations with suppliesappropriately.

Continuing with the example of a set of discrete restaurants in ageographically distributed network of restaurants, a specific restaurantmay be situated in a climactically warm area near a controlled-accesshighway access point. Because of its location near the controlled-accesshighway, the restaurant may consume some supplies at rates differentfrom those of other restaurants in the same network but located in towncenters. Each will also have different opportunity for re-supply becauseof different access to transportation infrastructure. For example, therestaurant near the controlled-access highway may consume more materialsused for drive-through delivery of food than a restaurant in a towncenter that hosts more dine-in customers. The restaurant near thecontrolled-access highway is much more subject to variances in businessgiven construction on the highway than a restaurant in the town center.Similarly, the restaurant in a town center is much more likely to havepower restored more quickly following an outage, given its proximity topopulation, than the restaurant near the controlled-access highway.

Using the system and methods described above, meta-data describing theconsumption patterns of various supplies by the different restaurantscan be stored in the asset database 420. For example, the part numbersof the various supplies, their consumption rates, the variation of theconsumption rate as a function of time of year, local supplies, andother similar information can be stored in the asset database 420.Similarly, risks to the supply, average delivery times, common carriercosts and non-delivery rates, power outage impacts can be stored in thelocal baseline database 424.

The other elements of the system then function in substantially the sameway as described above to determine the risk exposure value of a missedshipment as well as the risk exposure value of over-supplying therestaurant or carrying excess inventory at the restaurant. Thesecompeting factors can then be balanced in the order-fulfillment andshipment process and other business planning processes such as on-sitepower generation.

Computing Device

FIG. 5 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). Specifically, FIG. 5 shows adiagrammatic representation of a machine in the example form of acomputer system 500 within which instructions 524 (e.g., software) forcausing the machine to perform any one or more of the methodologiesdiscussed herein may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 524 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions524 to perform any one or more of the methodologies discussed herein.

The example computer system 500 includes a processor 502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 504, and astatic memory 506, which are configured to communicate with each othervia a bus 508. The computer system 500 may further include graphicsdisplay unit 510 (e.g., a plasma display panel (PDP), a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)). The computersystem 500 may also include alphanumeric input device 512 (e.g., akeyboard), a cursor control device 514 (e.g., a mouse, a trackball, ajoystick, a motion sensor, or other pointing instrument), a storage unit516, a signal generation device 518 (e.g., a speaker), and a networkinterface device 820, which also are configured to communicate via thebus 508.

The storage unit 516 includes a machine-readable medium 522 on which isstored instructions 524 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 524(e.g., software) may also reside, completely or at least partially,within the main memory 504 or within the processor 502 (e.g., within aprocessor's cache memory) during execution thereof by the computersystem 500, the main memory 504 and the processor 502 also constitutingmachine-readable media. The instructions 524 (e.g., software) may betransmitted or received over a network 526 via the network interfacedevice 520.

While machine-readable medium 522 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 524). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 524) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Risk exposure system 404, as well as its constituent components assetdatabase 420, local baseline database 424, query engine 428, combinedmap generator 432, risk factor database 436 and risk exposure valuecalculator 440 are, in various embodiments, implemented using one ormore computers configured such as computer 500 discussed above. Those ofskill in the art will recognize that based on processing requirements,several various components may be implemented on a common one of suchcomputers, or several of such computers can operate in a collaborativefashion to implement one or more of such components.

Other Considerations

While particular embodiments are described, it is to be understood thatmodifications will be apparent to those skilled in the art withoutdeparting from the spirit of the invention described herein. The scopeof the invention is not limited to the specific embodiments describedherein. Other embodiments, uses and advantages of the invention will beapparent to those skilled in art from consideration of the specificationand practice of the embodiments disclosed herein.

The embodiments herein have been described in particular detail withrespect to several possible embodiments. Those of skill in the art willappreciate that the invention may be practiced in other embodiments. Theparticular naming of the components, capitalization of terms, theattributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement various embodiments may have different names, formats, orprotocols. Further, the system may be implemented via a combination ofhardware and software, as described, or entirely in hardware elements.Also, the particular division of functionality between the varioussystem components described herein is merely exemplary, and notmandatory; functions performed by a single system component may insteadbe performed by multiple components, and functions performed by multiplecomponents may instead performed by a single component.

Some portions of above description present the features of theembodiments in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. These operations, while describedfunctionally or logically, are understood to be implemented by computerprograms. Furthermore, it has also proven convenient at times, to referto these arrangements of operations as modules or by functional names,without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “determining” or the like, refer tothe action and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the described embodiments include process steps andinstructions described herein in the form of an algorithm. It should benoted that various of the process steps and instructions disclosedherein could be embodied in software, firmware or hardware, and whenembodied in software, could be downloaded to reside on and be operatedfrom different platforms used by real time network operating systems.

The described embodiments also relate to an apparatus for performing theoperations herein. This apparatus may be specially constructed for thevarious purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer and run bya computer processor. Such a computer program may be stored in acomputer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, applicationspecific integrated circuits (ASICs), or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus. Furthermore, the computers referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

In addition, the described embodiments are not described with referenceto any particular programming language. It is appreciated that a varietyof programming languages may be used to implement the teachings asdescribed herein.

The described embodiments are well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructional purposesand may not have been selected to delineate or circumscribe theinventive subject matter. Accordingly, the disclosure is intended to beillustrative, but not limiting, of the scope of the invention.

What is claimed is:
 1. A method comprising: storing asset datarepresenting a network asset, wherein the network asset includes atleast a first portion associated with a first geographic location and asecond portion associated with a second geographic location, wherein thefirst geographic location is different from the second geographiclocation, the asset data including metadata characterizing the firstportion and metadata characterizing the second portion; storing contextdata representing a context of the network asset, wherein the context ofthe network asset includes a plurality of geographic features, thecontext data including metadata characterizing each geographic featureof the plurality of geographic features; generating risk datarepresenting risks posed by the context of the network asset to thenetwork asset, wherein generating the risk data comprises: identifying aplurality of intersection points between the network asset and thecontext of the network asset, wherein the plurality of intersectionpoints includes: a first subset of intersection points corresponding tointersections between the first portion of the network asset and theplurality of geographic features, and a second subset of intersectionpoints corresponding to intersections between the second portion of thenetwork asset and the plurality of geographic features; for eachintersection point of the plurality of intersection points: retrieving,from the asset data, metadata characterizing the network asset at alocation of the intersection point; determining, based on the contextdata, a particular geographic feature of the plurality of geographicfeatures at the location of the intersection point; retrieving, from thecontext data, metadata characterizing the particular geographic feature;determining, based on the metadata characterizing the network asset andthe metadata characterizing the particular geographic feature, a riskposed by the particular geographic feature to the network asset at thelocation of the intersection point; generating metadata associating therisk posed by the particular geographic feature with the network assetat the location of the intersection point.
 2. The method of claim 1,wherein the network asset includes a third portion associated with aplurality of possible geographic locations, wherein the asset dataincludes metadata characterizing the third portion at each possiblegeographic location of the plurality of possible geographic locations.3. The method of claim 1, further comprising, for each intersectionpoint of the plurality of intersection points: determining, based on themetadata characterizing the network asset and the metadatacharacterizing the particular geographic feature, a risk exposure valueassociated with the risk posed by the particular geographic feature tothe network asset; generating metadata associating the risk exposurevalue with the network asset at the location of the intersection point.4. The method of claim 3 wherein determining the risk exposure value isfurther based on a time of year.
 5. The method of claim 3 whereindetermining the risk exposure value is further based on the location ofthe intersection point.
 6. The method of claim 3 wherein the networkasset includes one or more moveable features, and wherein determiningthe risk exposure value is further based on a current location of eachmoveable feature of the one or more moveable features.
 7. The method ofclaim 3 wherein the network asset includes one or more moveablefeatures, and wherein determining the risk exposure value is furtherbased on a number of moveable features at the location of theintersection point.
 8. The method of claim 3 wherein the network assetincludes one or more moveable features, wherein each moveable feature ofthe one or more moveable features is associated with a respectiveplurality of possible geographic locations, and wherein determining therisk exposure value is further based on whether the location of theintersection point is a possible geographic location of at least onemoveable feature of the one or more moveable features.
 9. The method ofclaim 3 further comprising, for each intersection point, determining aweight associated with the intersection point, and wherein determiningthe risk exposure value is further based on the weight associated withthe intersection point.
 10. The method of claim 9 wherein determiningthe weight associated with the intersection point is based on at leastone of: a set of one or more rules, a set of one or more conditions, themetadata characterizing the network asset at the location of theintersection point, and the metadata characterizing the particulargeographic feature.
 11. A system comprising: one or more processor;non-transitory computer-readable media storing instructions which, whenexecuted by the one or more processors, cause: storing asset datarepresenting a network asset, wherein the network asset includes atleast a first portion associated with a first geographic location and asecond portion associated with a second geographic location, wherein thefirst geographic location is different from the second geographiclocation, the asset data including metadata characterizing the firstportion and metadata characterizing the second portion; storing contextdata representing a context of the network asset, wherein the context ofthe network asset includes a plurality of geographic features, thecontext data including metadata characterizing each geographic featureof the plurality of geographic features; generating risk datarepresenting risks posed by the context of the network asset to thenetwork asset, wherein generating the risk data comprises: identifying aplurality of intersection points between the network asset and thecontext of the network asset, wherein the plurality of intersectionpoints includes: a first subset of intersection points corresponding tointersections between the first portion of the network asset and theplurality of geographic features, and a second subset of intersectionpoints corresponding to intersections between the second portion of thenetwork asset and the plurality of geographic features; for eachintersection point of the plurality of intersection points: retrieving,from the asset data, metadata characterizing the network asset at alocation of the intersection point; determining, based on the contextdata, a particular geographic feature of the plurality of geographicfeatures at the location of the intersection point; retrieving, from thecontext data, metadata characterizing the particular geographic feature;determining, based on the metadata characterizing the network asset andthe metadata characterizing the particular geographic feature, a riskposed by the particular geographic feature to the network asset at thelocation of the intersection point; generating metadata associating therisk posed by the particular geographic feature with the network assetat the location of the intersection point.
 12. The system of claim 11,wherein the network asset includes a third portion associated with aplurality of possible geographic locations, wherein the asset dataincludes metadata characterizing the third portion at each possiblegeographic location of the plurality of possible geographic locations.13. The system of claim 11 wherein the instructions, when executed bythe one or more processors, further cause for each intersection point ofthe plurality of intersection points: determining, based on the metadatacharacterizing the network asset and the metadata characterizing theparticular geographic feature, a risk exposure value associated with therisk posed by the particular geographic feature to the network asset;generating metadata associating the risk exposure value with the networkasset at the location of the intersection point.
 14. The system of claim13 wherein determining the risk exposure value is further based on atime of year.
 15. The system of claim 13 wherein determining the riskexposure value is further based on the location of the intersectionpoint.
 16. The system of claim 13 wherein the network asset includes oneor more moveable features, and wherein determining the risk exposurevalue is further based on a current location of each moveable feature ofthe one or more moveable features.
 17. The system of claim 13 whereinthe network asset includes one or more moveable features, and whereindetermining the risk exposure value is further based on a number ofmoveable features at the location of the intersection point.
 18. Thesystem of claim 13 wherein the network asset includes one or moremoveable features, wherein each moveable feature of the one or moremoveable features is associated with a respective plurality of possiblegeographic locations, and wherein determining the risk exposure value isfurther based on whether the location of the intersection point is apossible geographic location of at least one moveable feature of the oneor more moveable features.
 19. The system of claim 13 wherein theinstructions, when executed by the one or more processors, furthercause, for each intersection point, determining a weight associated withthe intersection point, and wherein determining the risk exposure valueis further based on the weight associated with the intersection point.